The grain-size distributions of sediment deposits routed across the Earth surface contain valuable information about the source area they are derived from, the transport processes that moved them and the transport pathway. These information about the transport regime are however obscured when sediments become mixed during deposition. End-member modelling analysis or EMMA is a statistical approach to unmix the underlying transport regimes.

The figure above explains the principal setting of EMMA. From grain-size measurements classic statistic parameters based on the method of moments (e.g., mean, skewness, D50) can be calculated but are only meaningful for unimodal distributions. As soon as the samples consist of more than one unimodal distributions these need to be unmixed, either statistically (by EMMA) or by fitting parameteric models (e.g., the sum of log-normal distributions). The underlying concept of EMMA is that processes tend to sort sediments and create characteristic grain-size distributions. When several processes deposit sediments at a given place (a lake, an ocean, a loess deposit) the process signatures become mixed. EMMA describes such a mixed data set as a combination of end-member loadings and scores. Loadings are the fundamental grain-size distributions that build the data set and may be interpreted as a direct proxy for the responsible processes. Scores are the relative contributions of each loading to a sample and may be interpreted as the importance of each process in the formation of the deposit through time or space.

WHO BUILT EMMA?

The algorithm that is the heart of the R-package EMMAgeo was developed for Matlab by Elisabeth Dietze (references see below). I simply translated it to R and built a couple of functions around it to allow a comprehensive and convenient workflow in a free and open software environment. If you have questions about the EMMA algorithm, its concept and mathematical foundations, please contact Elisabeth directly. If you request technical support and need information about the functions of the R-package, see below and feel free to contact me.

HOW TO CITE THE PACKAGE

Currently we are working on a comprehensive reference article for the package. Until this is available, please use both of the following references to adequately acknowledge the package authors:

EMMAgeo is hosted on CRAN, the comprehensive R archive network. Thus, it is easy to install using

install.packages("EMMAgeo").

Currently, version 0.9.4 is online. The latest release with mostly minor additions is hosted on GitHub. There are a series of ways to install the package. The most convenient and coherent one is installing it through the R-package devtools.

Of course, this requires installing devtools first (e.g., by typing install.packages("devtools")). If you need help installing devtools, see the help page on the CRAN website. Alternatively, if you do not want or do not manage to work with devtools these versions can be downloaded (no guarantee for the very latest version, see time stamp on GitHub) and used for installation typing

EMMAgeo has seen quite a bit of success and wide application. Hence, there is material of differnt depth and scope available. Currently, we are working on a profound introduction and validation article.

If you feel helpless with an issue or notice a bug in one of the functions of the package, if you wish to have a further feature implemented, there is a good chance that I will engange with this issue, if you let me know of it. Please send me an email and I will add this to the list of issues shown here.